# Models, Page 211 of 525 Source: https://savrn.com/models/page/211 Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- SAVRN Model Hub # Open-Weight Models An open-weight model is an AI model whose trained weights are published for anyone to download. The weights are what the model learned in training. With a copy of them you can run the model on hardware you control and train it further on your own data. Open weights are not the same as open source. Many publishers release the weights without the training data or code, and the license sets what you may do with the model. This library puts each model's full card, architecture, files, license and published evaluations on one page. 12,579Models 3,583Datasets 291Papers 7,578Publishers 12,850Sourced relationships Updated 2026-10-23 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,548](https://savrn.com/models?task=Text+generation) - [Image and text to text577](https://savrn.com/models?task=Image+and+text+to+text) - [Feature extraction398](https://savrn.com/models?task=Feature+extraction) - [Robotics398](https://savrn.com/models?task=Robotics) - [Text classification331](https://savrn.com/models?task=Text+classification) - [Text to image232](https://savrn.com/models?task=Text+to+image) - [Speech recognition187](https://savrn.com/models?task=Speech+recognition) - [Text to speech145](https://savrn.com/models?task=Text+to+speech) - [Image classification139](https://savrn.com/models?task=Image+classification) - [Audio classification130](https://savrn.com/models?task=Audio+classification) - 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[cc-by-nc-nd-4.014](https://savrn.com/models?license=cc-by-nc-nd-4.0) - [llama314](https://savrn.com/models?license=llama3) - [openrail14](https://savrn.com/models?license=openrail) - [llama28](https://savrn.com/models?license=llama2) - [afl-3.07](https://savrn.com/models?license=afl-3.0) - [osl-3.06](https://savrn.com/models?license=osl-3.0) - [cc0-1.04](https://savrn.com/models?license=cc0-1.0) Access - [Open weights12,153](https://savrn.com/models?access=open_access) - [Access requested at publisher426](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher328](https://savrn.com/models?publisher=mradermacher) - [Yurun Yuan115](https://savrn.com/models?publisher=ryanyr) - [RunningHub.ai105](https://savrn.com/models?publisher=runninghubai) - [Qwen91](https://savrn.com/models?publisher=qwen) - [Jenny Wilson85](https://savrn.com/models?publisher=jenny08311) - [Jessen Pu77](https://savrn.com/models?publisher=putty77) - [RKNNAI73](https://savrn.com/models?publisher=rknnai) - [AI at Meta72](https://savrn.com/models?publisher=facebook) - [Convergent Intelligence66](https://savrn.com/models?publisher=reaperdoesntknow) - [Yurun Yuan65](https://savrn.com/models?publisher=yurunyyr) - [Google64](https://savrn.com/models?publisher=google) - [Yurun Yuan62](https://savrn.com/models?publisher=yuruny) - [Firza Hadzami61](https://savrn.com/models?publisher=firzahdzm) - [Zeyuan Yang59](https://savrn.com/models?publisher=miiche) - [Yurun Yuan58](https://savrn.com/models?publisher=yyuan244) - [Yanggangu57](https://savrn.com/models?publisher=yanggangu) - [Hongao56](https://savrn.com/models?publisher=cisco1963) - [Christopher Subia-Waud55](https://savrn.com/models?publisher=cwaud) - [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805) - [Khazzz1c53](https://savrn.com/models?publisher=khazic) - [Alfred Smith52](https://savrn.com/models?publisher=alfred8995) - [Szekely52](https://savrn.com/models?publisher=attila115) - [NVIDIA50](https://savrn.com/models?publisher=nvidia) - [Wang49](https://savrn.com/models?publisher=davidwdw) [Most downloaded](https://savrn.com/models?sort=downloads)[Most liked](https://savrn.com/models?sort=likes)[Recently updated](https://savrn.com/models?sort=updated)[Name](https://savrn.com/models?sort=name)[Largest](https://savrn.com/models?sort=params) 12,579 models, sorted by most downloaded. Model · Text to image ### [Qwen-Image-2.1-Uncensored-GGUF](https://savrn.com/models/qwen-image-2-1-uncensored-gguf-3) [Maxim Fedyay](https://savrn.com/model-publishers/maximulya) GGUF quantizations of Qwen/Qwen-Image-2.1 for local image generation using the original upstream base weights. Q4KM is recommended for the best balance of size and quality. Companion model files packaged for ComfyUI: Use the model with ComfyUI and ComfyUI-GGUF. All required companion files (GGUF transformer, text encoder, and VAE) are hosted directly in this repository. Download the files and place them in their respective ComfyUI directories: 1. Install ComfyUI-GGUF: Use the maintained fork with native Qwen-Image 2.1 support by cloning leejet/ComfyUI-GGUF into your custom nodes: (Note: If you have the older city96/ComfyUI-GGUF installed and encounter an Unknown model architecture! error… Access requested at publisher other gguf [View model](https://savrn.com/models/qwen-image-2-1-uncensored-gguf-3) Model · Text to image ### [Qwen-Image-2.1-Uncensored-GGUF-Ultraoptimised-Curated](https://savrn.com/models/qwen-image-2-1-uncensored-gguf-ultraoptimised-curated) [Solstice-AI](https://savrn.com/model-publishers/solstice-ai) Open weights other gguf [View model](https://savrn.com/models/qwen-image-2-1-uncensored-gguf-ultraoptimised-curated) Model · Text to image ### [Qwen-Image-2.1-Uncensored-GGUF-backup-best](https://savrn.com/models/qwen-image-2-1-uncensored-gguf-backup-best) [Nexylll](https://savrn.com/model-publishers/nexylll) GGUF quantizations of Qwen/Qwen-Image-2.1 for local image generation using the original upstream base weights. Q4KM is recommended for the best balance of size and quality. Companion model files packaged for ComfyUI: Use the model with ComfyUI and ComfyUI-GGUF. All required companion files (GGUF transformer, text encoder, and VAE) are hosted directly in this repository. Download the files and place them in their respective ComfyUI directories: 1. Install ComfyUI-GGUF: Use the maintained fork with native Qwen-Image 2.1 support by cloning leejet/ComfyUI-GGUF into your custom nodes: (Note: If you have the older city96/ComfyUI-GGUF installed and encounter an Unknown model architecture! error… Open weights other gguf [View model](https://savrn.com/models/qwen-image-2-1-uncensored-gguf-backup-best) Model · Text to image ### [Qwen-Image-2.1-Uncensored-OpenVINO-INT4](https://savrn.com/models/qwen-image-2-1-uncensored-openvino-int4) [Blah Blah](https://savrn.com/model-publishers/blaj) OpenVINO IR build of Qwen-Image-2.1 with an abliterated text encoder, quantized to INT4. Runs text-to-image on Intel Arc iGPUs. Uncensoring lives in the prompt-embedding path, so only the Qwen3-VL text encoder needed replacing — the transformer, VAE and vision encoder are the stock model. Total ~13 GB. Embeddings for "a portrait of a woman, photorealistic" differ decisively from the stock encoder — this is not a renamed copy: Intel Core Ultra 7 258V, Arc 130V/140V iGPU, 8 cores, 30 GB RAM: Requires the nightly stack: diffusers and optimum-intel from main, plus OpenVINO 2026.5.0 nightly. See the full conversion write-up… Open weights other openvino [View model](https://savrn.com/models/qwen-image-2-1-uncensored-openvino-int4) Model ### [Qwen-Image-2.1-turbo-comfy](https://savrn.com/models/qwen-image-2-1-turbo-comfy) [Vftfnn](https://savrn.com/model-publishers/vftfnn) Open weights other diffusion-single-file [View model](https://savrn.com/models/qwen-image-2-1-turbo-comfy) Model · Text to image ### [Qwen-Image-2.1-turbo-mfd](https://savrn.com/models/qwen-image-2-1-turbo-mfd) [D](https://savrn.com/model-publishers/congruency) v0.1 - Rank 32 LoRA trained with Mean Flow Distillation up to 2.1 megapixels Open weights other diffusers [View model](https://savrn.com/models/qwen-image-2-1-turbo-mfd) Model · Image to image ### [Qwen-Image-Edit-2509](https://savrn.com/models/qwen-image-edit-2509) [Throw](https://savrn.com/model-publishers/smallthrow22) This September, we are pleased to introduce Qwen-Image-Edit-2509, the monthly iteration of Qwen-Image-Edit. To experience the latest model, please visit Qwen Chat and select the "Image Editing" feature. Compared with Qwen-Image-Edit released in August, the main improvements of Qwen-Image-Edit-2509 include: Install the latest version of diffusers The following contains a code snippet illustrating how to use Qwen-Image-Edit-2509: The primary update in Qwen-Image-Edit-2509 is support for multi-image inputs. Let’s first look at a "person + person" example: Here is a "person + scene" example: Below is a "person + object" example: In fact, multi-image input also supports commonly used ControlNet… Open weights apache-2.0 20.4B parameters diffusers [View model](https://savrn.com/models/qwen-image-edit-2509) Model ### [Qwen-Qwen2.5-VL-3B-Instruct](https://savrn.com/models/qwen-qwen2-5-vl-3b-instruct) [Hisham](https://savrn.com/model-publishers/mscabdelrahman1) Open weights [View model](https://savrn.com/models/qwen-qwen2-5-vl-3b-instruct) Model ### [Qwen1.5-MoE-A2.7B-SNLI-Task-Only](https://savrn.com/models/qwen1-5-moe-a2-7b-snli-task-only) [Dinhtracducanh](https://savrn.com/model-publishers/ducanhdinh) Day la LoRA adapter finetune tu Qwen/Qwen1.5-MoE-A2.7B, mot mo hinh Mixture-of-Experts, tren task SNLI (Stanford Natural Language Inference) — phan loai quan he giua premise/hypothesis thanh entailment / neutral / contradiction. (tuong ung khoang 1L/3 -> 2L/3), giong het setting cua code alignment finetuning. thanh phan mot rank rieng qua rankpattern cua PEFT: - alpha = 32, dropout = 0.05 Ltotal = LLM + lblosscoef LLB - LLM: cross-entropy chuan tren token tiep theo, CHI tinh tren phan (prompt bi mask, giong instruction-tuning/SFT tieu chuan). - LLB: load balancing loss chuan cua MoE (Switch/Mixtral style), tinh tren cac router nam trong khoang layer duoc finetune. - lblosscoef (lambda) =… Open weights apache-2.0 peft [View model](https://savrn.com/models/qwen1-5-moe-a2-7b-snli-task-only) Model · Text generation ### [Qwen2.5-0.5B-Instruct-CodeInfused-IQ3_XXS-GGUF](https://savrn.com/models/qwen2-5-0-5b-instruct-codeinfused-iq3-xxs-gguf) [DuoNeural](https://savrn.com/model-publishers/duoneural) Calibrated with DuoNeural 131k-token code-infused activation Hessian (qwen05bgtap.imatrix), preserving micro-model syntax and reasoning under aggressive sub-3.5-bit compression. G-TAP treats neural network post-training quantization through non-equilibrium statistical mechanics: 1. Onsager Cavity Field Correction: Subtracts uncentered activation self-interaction from weight tensors, preventing 1-step Replica Symmetry Breaking (1-RSB) glass freezing. 2. Replicon Convexity Bound: Enforces stability along the de Almeida-Thouless line. 3. Radial Gain Conservation: Guarantees entry-wise variance preservation and zero signal degradation. Open weights apache-2.0 transformers [View model](https://savrn.com/models/qwen2-5-0-5b-instruct-codeinfused-iq3-xxs-gguf) Model · Text generation ### [Qwen2.5-0.5B-Instruct-GTAP-v3-IQ3_XXS-GGUF](https://savrn.com/models/qwen2-5-0-5b-instruct-gtap-v3-iq3-xxs-gguf) [DuoNeural](https://savrn.com/model-publishers/duoneural) Pre-conditioned with Generalized Thouless-Anderson-Palmer (G-TAP v3) Onsager cavity damping on SwiGLU Wdown, enforcing replica symmetry across micro-parameter manifolds. G-TAP treats neural network post-training quantization through non-equilibrium statistical mechanics: 1. Onsager Cavity Field Correction: Subtracts uncentered activation self-interaction from weight tensors, preventing 1-step Replica Symmetry Breaking (1-RSB) glass freezing. 2. Replicon Convexity Bound: Enforces stability along the de Almeida-Thouless line. 3. Radial Gain Conservation: Guarantees entry-wise variance preservation and zero signal degradation. Open weights apache-2.0 transformers [View model](https://savrn.com/models/qwen2-5-0-5b-instruct-gtap-v3-iq3-xxs-gguf) Model · Text generation ### [Qwen2.5-0.5B-Instruct-GTAP-v3-Q4_K_M-GGUF](https://savrn.com/models/qwen2-5-0-5b-instruct-gtap-v3-q4-k-m-gguf) [DuoNeural](https://savrn.com/model-publishers/duoneural) Production 4-bit edge quant with G-TAP v3 pre-conditioning, running at over 800+ tokens/second on modern GPUs and edge NPUs. G-TAP treats neural network post-training quantization through non-equilibrium statistical mechanics: 1. Onsager Cavity Field Correction: Subtracts uncentered activation self-interaction from weight tensors, preventing 1-step Replica Symmetry Breaking (1-RSB) glass freezing. 2. Replicon Convexity Bound: Enforces stability along the de Almeida-Thouless line. 3. Radial Gain Conservation: Guarantees entry-wise variance preservation and zero signal degradation. Open weights apache-2.0 transformers [View model](https://savrn.com/models/qwen2-5-0-5b-instruct-gtap-v3-q4-k-m-gguf) Model · Text generation ### [Qwen2.5-0.5B-Q5_0-GGUF](https://savrn.com/models/qwen2-5-0-5b-q5-0-gguf) [Des](https://savrn.com/model-publishers/zed122) This model was converted to GGUF format from Qwen/Qwen2.5-0.5B using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Install llama.cpp through brew. Invoke the llama.cpp server or the CLI. Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Open weights apache-2.0 transformers [View model](https://savrn.com/models/qwen2-5-0-5b-q5-0-gguf) Model · Text generation ### [Qwen2.5-1.5B-Instruct-CodeInfused-IQ3_XXS-GGUF](https://savrn.com/models/qwen2-5-1-5b-instruct-codeinfused-iq3-xxs-gguf) [DuoNeural](https://savrn.com/model-publishers/duoneural) Calibrated with DuoNeural 131k-token code-infused activation Hessian (qwen15gtap.imatrix), preserving small-language-model syntax and reasoning under aggressive sub-3.5-bit compression. G-TAP treats neural network post-training quantization through non-equilibrium statistical mechanics: 1. Onsager Cavity Field Correction: Subtracts uncentered activation self-interaction from weight tensors, preventing 1-step Replica Symmetry Breaking (1-RSB) glass freezing. 2. Replicon Convexity Bound: Enforces stability along the de Almeida-Thouless line. 3. Radial Gain Conservation: Guarantees entry-wise variance preservation and zero signal degradation. Open weights apache-2.0 transformers [View model](https://savrn.com/models/qwen2-5-1-5b-instruct-codeinfused-iq3-xxs-gguf) Model · Text generation ### [Qwen2.5-1.5B-Instruct-Conceptizer](https://savrn.com/models/qwen2-5-1-5b-instruct-conceptizer) [Haoran Zheng](https://savrn.com/model-publishers/flyingodzilla) Open weights apache-2.0 peft [View model](https://savrn.com/models/qwen2-5-1-5b-instruct-conceptizer) Model · Text generation ### [Qwen2.5-1.5B-Instruct-GTAP-v3-IQ3_XXS-GGUF](https://savrn.com/models/qwen2-5-1-5b-instruct-gtap-v3-iq3-xxs-gguf) [DuoNeural](https://savrn.com/model-publishers/duoneural) Pre-conditioned with Generalized Thouless-Anderson-Palmer (G-TAP v3) Onsager cavity damping on SwiGLU Wdown, enforcing replica symmetry across micro-parameter manifolds. G-TAP treats neural network post-training quantization through non-equilibrium statistical mechanics: 1. Onsager Cavity Field Correction: Subtracts uncentered activation self-interaction from weight tensors, preventing 1-step Replica Symmetry Breaking (1-RSB) glass freezing. 2. Replicon Convexity Bound: Enforces stability along the de Almeida-Thouless line. 3. Radial Gain Conservation: Guarantees entry-wise variance preservation and zero signal degradation. Open weights apache-2.0 transformers [View model](https://savrn.com/models/qwen2-5-1-5b-instruct-gtap-v3-iq3-xxs-gguf) Model · Text generation ### [Qwen2.5-1.5B-Instruct-GTAP-v3-Q4_K_M-GGUF](https://savrn.com/models/qwen2-5-1-5b-instruct-gtap-v3-q4-k-m-gguf) [DuoNeural](https://savrn.com/model-publishers/duoneural) Production 4-bit edge quant with G-TAP v3 pre-conditioning, achieving 86.7% code AST, 100% Hermes tool calling, and running at over 230+ tokens/second on modern GPUs and edge NPUs. G-TAP treats neural network post-training quantization through non-equilibrium statistical mechanics: 1. Onsager Cavity Field Correction: Subtracts uncentered activation self-interaction from weight tensors, preventing 1-step Replica Symmetry Breaking (1-RSB) glass freezing. 2. Replicon Convexity Bound: Enforces stability along the de Almeida-Thouless line. 3. Radial Gain Conservation: Guarantees entry-wise variance preservation and zero signal degradation. Open weights apache-2.0 transformers [View model](https://savrn.com/models/qwen2-5-1-5b-instruct-gtap-v3-q4-k-m-gguf) Model · Text generation ### [Qwen2.5-1.5B-Instruct-gptq-4bit-int4-awq](https://savrn.com/models/qwen2-5-1-5b-instruct-gptq-4bit-int4-awq) [Domenic Federico](https://savrn.com/model-publishers/dfed24) Status: exported, not yet verified on a GPU. This checkpoint is in the AutoAWQ GEMM layout (4 bits, group size 64, asymmetric with integer zero points) so that vLLM's awq / awqmarlin kernels can load it. The weights come from the recipe in github.com/dfed25/mlx-gptq run with --zero-point int: error-feedback rounding (GPTQ), an alternating grid fit, coordinate-descent refinement and a weighted refit, with every group's offset constrained to a whole number of steps from the start, so that conversion to this format changes the weights by 0.05% (mean relative), against about 10% when a free-offset model is converted afterwards. Measured on the MLX version of the same weights (MacBook Pro M4… Open weights apache-2.0 1.5B parameters 32,768 tokens [View model](https://savrn.com/models/qwen2-5-1-5b-instruct-gptq-4bit-int4-awq) Model ### [Qwen2.5-1.5B-Instruct.Q4_K_M.gguf](https://savrn.com/models/qwen2-5-1-5b-instruct-q4-k-m-gguf) [Haijun AI](https://savrn.com/model-publishers/haijun-ai) Open weights [View model](https://savrn.com/models/qwen2-5-1-5b-instruct-q4-k-m-gguf) Model ### [Qwen2.5-1.5B-Instruct_SmartCockpit_V2](https://savrn.com/models/qwen2-5-1-5b-instruct-smartcockpit-v2) [Chen Marcus](https://savrn.com/model-publishers/marcus0304) This model is a fine-tuned version of unsloth/Qwen2.5-1.5B-Instruct. It has been trained using TRL. This model was trained with SFT. Open weights transformers [View model](https://savrn.com/models/qwen2-5-1-5b-instruct-smartcockpit-v2) Model ### [Qwen2.5-1.5B-Instruct_SmartCockpit_V3](https://savrn.com/models/qwen2-5-1-5b-instruct-smartcockpit-v3) [Chen Marcus](https://savrn.com/model-publishers/marcus0304) This model is a fine-tuned version of unsloth/Qwen2.5-1.5B-Instruct. It has been trained using TRL. This model was trained with SFT. Open weights transformers [View model](https://savrn.com/models/qwen2-5-1-5b-instruct-smartcockpit-v3) Model · Text generation ### [Qwen2.5-14B_Uncencored_Instruct-GGUF](https://savrn.com/models/qwen2-5-14b-uncencored-instruct-gguf) [Khairul Zakry](https://savrn.com/model-publishers/zecro) pipelinetag: text-generation This is quantized version of SicariusSicariiStuff/Qwen2.5-14BUncencoredInstruct created using llama.cpp This is the instruct model, except my uncensoring protocol I also added some RP data, Story writing and tried to nudge the model towards aggressiveness, for more spicy RP and story bias. The intetion was to make a base that is easier to finetune on with tasks such as RP and writing. - pending / 10 (10 completely uncensored) - My Ko-fi page ALL donations will go for research resources and compute, every bit is appreciated - Blog and updates Some updates, some rambles, sort of a mix between a diary and a blog. - LLAMA-38BUnaligned The grand project that started… Open weights apache-2.0 [View model](https://savrn.com/models/qwen2-5-14b-uncencored-instruct-gguf) Model ### [Qwen2.5-14B_nas_bank](https://savrn.com/models/qwen2-5-14b-nas-bank) [Jingfeng Fengjing](https://savrn.com/model-publishers/jingfengya) Open weights [View model](https://savrn.com/models/qwen2-5-14b-nas-bank) Model ### [Qwen2.5-32B-graft0-a1-terra3k-r3000-da-e1](https://savrn.com/models/qwen2-5-32b-graft0-a1-terra3k-r3000-da-e1) [SecondLookResearch](https://savrn.com/model-publishers/secondlookresearch) Difficult advice (terra 3k ladder, rung 3000 rows), 1 epoch from scratch, fresh linear-only LoRA r64/a128 over frozen graft0-a1, warmup 5% (floor 2 steps). Rows are a seed-0 prefix of the same shuffle as the other rungs; val = terra3k-val-qwen25.jsonl. LoRA r64/a128, linear-only, graft0 platform. Difficult-advice stage trained as a FRESH adapter over a merged, frozen A1. Serve as TWO adapters on the patched base, A1 first, then this one: ARM= ROWPATCH=1 ADAPTERS=" " \ bash code/msmeval/servereconstructed.sh Open weights peft [View model](https://savrn.com/models/qwen2-5-32b-graft0-a1-terra3k-r3000-da-e1) ## Model Collections Hand-picked starting points, each with the reason it exists. Collection · 4 entries ### [Embedding models for retrieval](https://savrn.com/collections/embedding-models-for-retrieval) Sentence and document embedding models used to build retrieval systems. Dimension and sequence length matter more than size here, and both come from the publisher. [View collection](https://savrn.com/collections/embedding-models-for-retrieval) Collection · 4 entries ### [Models that fit on one accelerator](https://savrn.com/collections/models-that-run-on-one-accelerator) Models whose publisher-reported parameter count puts them within reach of a single accelerator at common precisions. Memory needed depends on precision and serving configuration, so treat the parameter count as the starting point, not the answer. [View collection](https://savrn.com/collections/models-that-run-on-one-accelerator) Collection · 6 entries ### [Open-weight text models worth knowing](https://savrn.com/collections/open-weight-text-models-worth-knowing) Widely used open-weight language models, chosen because each one is a distinct family rather than a variant of the one above it. Selection, not a ranking. [View collection](https://savrn.com/collections/open-weight-text-models-worth-knowing) Collection · 3 entries ### [Speech and audio models](https://savrn.com/collections/speech-and-audio) Recognition and synthesis models, grouped so the two directions are easy to compare. [View collection](https://savrn.com/collections/speech-and-audio) ## Open-Weight Models Explained ### What is an open-weight model? An AI model whose trained weights are published for anyone to download, so it can be run, tested and fine-tuned on hardware the user controls. ### Is an open-weight model the same as open source? Not always. Open weights means the trained model can be downloaded. Open source usually also means the training code and data are available and the license allows broad reuse. Many open-weight models release the weights only. ### Can I use an open-weight model commercially? It depends on the license. Apache 2.0 and MIT allow commercial use. Other licenses limit it, for example to non-commercial use or below a set number of users. Every model page here shows its license. ### How much memory does an open-weight model need? About two bytes per parameter at 16-bit precision, so a 7-billion-parameter model needs roughly 14 GB for its weights, plus memory for the context it processes. Each model page lists its parameter count and the size of its files. ## Related SAVRN Research The hub sits beside SAVRN's market data and infrastructure research: what models cost to run, and what it takes to run them. SAVRN Index ### [What open models cost to run](https://savrn.com/ai-index/pricing/open-models) The same open-weight model priced by every host that serves it, per million tokens. Research Hub ### [Data center trackers and maps](https://savrn.com/data-center-research) Moratoriums, permits, power, water and capital behind the facilities that run these models. Method ### [How the Model Hub is built](https://savrn.com/model-hub/methodology) Sources, evidence labels, refresh behaviour, and the limits of every comparison here.